TY - JOUR AU - Kim, Youngkyoung AU - Kim, Misoo AU - Lee, Eunseok PY - 2022/06/28 Y2 - 2024/03/28 TI - Tracking Down Misguiding Terms for Locating Bugs in Deep Learning-Based Software (Student Abstract) JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 36 IS - 11 SE - AAAI Student Abstract and Poster Program DO - 10.1609/aaai.v36i11.21628 UR - https://ojs.aaai.org/index.php/AAAI/article/view/21628 SP - 12983-12984 AB - Bugs in source files (SFs) may cause software malfunction, inconveniencing users and even leading to catastrophic accidents. Therefore, the bugs in SFs should be found and fixed quickly. However, from hundreds of candidate SFs, finding buggy SFs is tedious and time consuming. To lessen the burden on developers, deep learning-based bug localization (DLBL) tools can be utilized. Text terms in bug reports and SFs play an important role. However, some terms provide incorrect information and degrade bug localization performance. Therefore, those terms are defined here as "misguiding terms," and an explainable-artificial-intelligence-based identification method is proposed. The effectiveness of the proposed method for DLBL was investigated. When misguiding terms were removed, the mean average precision of the bug localization model improved by 33% on average. ER -